Note: some texts in this report are based on the book Orchestrating Single-Cell Analysis with Bioconductor published under CC BY 4.0
Project: Integration of 1k and 3k PBMC
- Institute: Example institute
- Laboratory: Example laboratory
- People: Example person 1, Example person 2
1000 and 3000 peripheral blood mononuclear cells by 10x Genomics
- Organism: human
Input data overview
This table is showing which data were integrated. Before integration, these data were processed by the single-sample pipeline.
The first step in integration was subsetting the input data to contain only a common set of features (“universe”):
Common features count: 8691
Then each sample was rescaled to adjust for differences in sequencing
depth. The batchelor::multiBatchNorm() function recomputes
log-normalized expression values after adjusting the size factors for
systematic differences in coverage between
SingleCellExperiment objects. (Size factors only remove
biases between cells within a single batch.) This improves the quality
of the correction by removing one aspect of the technical differences
between batches.
Show used functions ▾
Highly variable genes (HVGs) selection
HVGs identified in individual samples are combined before integration.
6 cell cycle related genes were removed before HVG selection.
Show cell cycle-related genes ▾
| ENSEMBL | SYMBOL |
|---|---|
| ENSG00000132780 | NASP |
| ENSG00000162607 | USP1 |
| ENSG00000143815 | LBR |
| ENSG00000164104 | HMGB2 |
| ENSG00000188229 | TUBB4B |
| ENSG00000189159 | JPT1 |
HVG metric (common for all samples): ‘gene_var’
-> combined by scran::combineVar()
Based on “gene_var”, HVGs were selected by: top 3000 HVGs.
All HVGs
Found 3000 HVGs.
HVGs with removed CC genes
Found 3000 HVGs.
Show used functions ▾
PCA selection
- Elbow point method: a simple heuristic for choosing PCs involves identifying the elbow point in the percentage of variance explained by successive PCs. This refers to the “elbow” in the curve of a scree plot as shown.
- Technical variance method: use the technical component estimates to
determine the proportion of variance that should be retained. This is
implemented in
scran::denoisePCA(), which takes the estimates returned byscran::modelGeneVar(). - Forced: use a predefined number of PCs.
- Corrected: Use the number of PCs calculated during the integration.
Harmony
With all HVGs
50 PCs were selected using the ‘NA’ method.
Removed cell cycle genes from HVGs
50 PCs were selected using the ‘NA’ method.
Mutual nearest neighbors
(fastMNN())
With all HVGs
50 PCs were selected using the ‘corrected’ method.
Removed cell cycle genes from HVGs
50 PCs were selected using the ‘corrected’ method.
Linear regression
(regressBatches())
With all HVGs
50 PCs were selected using the ‘corrected’ method.
Removed cell cycle genes from HVGs
50 PCs were selected using the ‘corrected’ method.
Rescaling (rescaleBatches())
With all HVGs
15 PCs were selected using the ‘forced’ method.
Removed cell cycle genes from HVGs
15 PCs were selected using the ‘forced’ method.
Uncorrected (multiBatchNorm())
With all HVGs
15 PCs were selected using the ‘forced’ method.
Removed cell cycle genes from HVGs
15 PCs were selected using the ‘forced’ method.
Show used functions ▾
Integration dimreds
Here you can quickly check how samples overlap after integration.
Harmony
Show method details ▾
Used function: harmony::RunHarmony()
Harmony projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony takes PCA matrix as the input and calculates new reduced dimensions that are corrected for batch effect and subsequently used for downstream steps (UMAP, t-SNE, clustering). That is, Harmony does not calculate a corrected expression matrix.
More details in Nature Methods
Mutual nearest neighbors
(fastMNN())
Show method details ▾
Used function: batchelor::fastMNN()
Mutual nearest neighbors (MNN) are pairs of cells from different batches that belong in each other’s set of nearest neighbors. The reasoning is that MNN pairs represent cells from the same biological state prior to the application of a batch effect - see Haghverdi et al. (2018) for full theoretical details. Thus, the difference between cells in MNN pairs can be used as an estimate of the batch effect, the subtraction of which yields batch-corrected values.
Compared to linear regression, MNN correction does not assume that the population composition is the same or known beforehand. This is because it learns the shared population structure via identification of MNN pairs and uses this information to obtain an appropriate estimate of the batch effect. Instead, the key assumption of MNN-based approaches is that the batch effect is orthogonal to the biology in high-dimensional expression space. Violations reduce the effectiveness and accuracy of the correction, with the most common case arising from variations in the direction of the batch effect between clusters. Nonetheless, the assumption is usually reasonable as a random vector is very likely to be orthogonal in high-dimensional space.
More details in OSCA
Removed cell cycle genes from HVGs
Linear regression
(regressBatches())
Show method details ▾
Used function: batchelor::regressBatches()
Batch effects in bulk RNA sequencing studies are commonly removed with linear regression. This involves fitting a linear model to each gene’s expression profile, setting the undesirable batch term to zero and recomputing the observations sans the batch effect, yielding a set of corrected expression values for downstream analyses.
To use this approach in a scRNA-seq context, we assume that the composition of cell subpopulations is the same across batches. We also assume that the batch effect is additive, i.e., any batch-induced fold-change in expression is the same across different cell subpopulations for any given gene. These are strong assumptions as batches derived from different individuals will naturally exhibit variation in cell type abundances and expression. Nonetheless, they may be acceptable when dealing with batches that are technical replicates generated from the same population of cells. (In fact, when its assumptions hold, linear regression is the most statistically efficient as it uses information from all cells to compute the common batch vector.) Linear modelling can also accommodate situations where the composition is known a priori by including the cell type as a factor in the linear model, but this situation is even less common.
More details in OSCA
Removed cell cycle genes from HVGs
Rescaling (rescaleBatches())
Show method details ▾
Used function: batchelor::rescaleBatches()
We use the rescaleBatches() function from the batchelor
package to remove the batch effect. This is roughly equivalent to
applying a linear regression to the log-expression values per gene, with
some adjustments to improve performance and efficiency. For each gene,
the mean expression in each batch is scaled down until it is equal to
the lowest mean across all batches. We deliberately choose to scale all
expression values down as this mitigates differences in variance when
batches lie at different positions on the mean-variance trend.
(Specifically, the shrinkage effect of the pseudo-count is greater for
smaller counts, suppressing any differences in variance across
batches.)
Removed cell cycle genes from HVGs
Uncorrected (multiBatchNorm())
Show method details ▾
Used function: batchelor::multiBatchNorm()
Data were rescaled to adjust for differences in sequencing depth
between samples. These data will be used for identification of cluster
markers (stage cluster_markers) and differential expression
analysis (stage contrasts).
Removed cell cycle genes from HVGs
Integration diagnostics
Graph-based clustering was used for diagnostics below.
Harmony
With all HVGs
Cells’s assignment
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 19.0% (224) | 12.2% (340) |
| 2 | 10.6% (125) | 9.2% (256) |
| 3 | 0.7% (8) | 0.4% (12) |
| 4 | 15.7% (185) | 23.1% (642) |
| 5 | 9.5% (112) | 6.7% (185) |
| 6 | 4.6% (54) | 5.6% (155) |
| 7 | 15.3% (181) | 12.5% (347) |
| 8 | 1.9% (22) | 1.1% (30) |
| 9 | 2.7% (32) | 5.4% (149) |
| 10 | 1.6% (19) | 1.2% (33) |
| 11 | 14.6% (172) | 19.2% (534) |
| 12 | 0.7% (8) | 0.1% (4) |
| 13 | 1.6% (19) | 2.3% (65) |
| 14 | 1.7% (20) | 1.0% (27) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 9 | 32 | 149 | 0.3766248 |
| 12 | 8 | 4 | 0.3745522 |
| 1 | 224 | 340 | 0.1873182 |
| 8 | 22 | 30 | 0.1679094 |
| 14 | 20 | 27 | 0.1648747 |
| 4 | 185 | 642 | 0.1490095 |
| 5 | 112 | 185 | 0.1151456 |
| 13 | 19 | 65 | 0.0916155 |
| 11 | 172 | 534 | 0.0753191 |
| 3 | 8 | 12 | 0.0564023 |
| 10 | 19 | 33 | 0.0514235 |
| 7 | 181 | 347 | 0.0406645 |
| 6 | 54 | 155 | 0.0339520 |
| 2 | 125 | 256 | 0.0181615 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.8191378 | 0.6748258 |
Removed cell cycle genes from HVGs
Cells’s assignment
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 17.4% (206) | 10.8% (299) |
| 2 | 2.3% (27) | 1.5% (42) |
| 3 | 9.6% (113) | 8.9% (247) |
| 4 | 15.4% (182) | 21.6% (599) |
| 5 | 3.1% (37) | 6.2% (171) |
| 6 | 4.6% (54) | 5.6% (156) |
| 7 | 15.3% (181) | 12.5% (347) |
| 8 | 10.5% (124) | 7.4% (205) |
| 9 | 0.7% (8) | 0.5% (14) |
| 10 | 1.9% (22) | 1.0% (29) |
| 11 | 15.2% (179) | 20.6% (573) |
| 12 | 1.7% (20) | 2.3% (65) |
| 13 | 0.7% (8) | 0.1% (4) |
| 14 | 1.7% (20) | 1.0% (28) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 5 | 37 | 171 | 0.3802113 |
| 13 | 8 | 4 | 0.3745522 |
| 1 | 206 | 299 | 0.2258289 |
| 10 | 22 | 29 | 0.1876631 |
| 14 | 20 | 28 | 0.1455313 |
| 8 | 124 | 205 | 0.1159271 |
| 4 | 182 | 599 | 0.1108979 |
| 2 | 27 | 42 | 0.1100458 |
| 11 | 179 | 573 | 0.0931570 |
| 12 | 20 | 65 | 0.0689663 |
| 7 | 181 | 347 | 0.0406645 |
| 6 | 54 | 156 | 0.0362057 |
| 9 | 8 | 14 | 0.0262408 |
| 3 | 113 | 247 | 0.0050848 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.8206348 | 0.6874412 |
Mutual nearest neighbors
(fastMNN())
With all HVGs
Cells’s assignment
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 16.2% (191) | 15.4% (428) |
| 2 | 15.6% (184) | 12.6% (349) |
| 3 | 12.1% (143) | 3.2% (90) |
| 4 | 1.9% (23) | 1.2% (34) |
| 5 | 2.1% (25) | 0.6% (17) |
| 6 | 14.9% (176) | 19.7% (548) |
| 7 | 15.6% (184) | 23.2% (646) |
| 8 | 0.7% (8) | 0.1% (4) |
| 9 | 2.6% (31) | 5.9% (163) |
| 10 | 0.7% (8) | 0.3% (7) |
| 11 | 4.1% (48) | 5.9% (165) |
| 12 | 7.1% (84) | 5.1% (143) |
| 13 | 4.7% (56) | 5.7% (158) |
| 14 | 1.7% (20) | 1.0% (27) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 3 | 143 | 90 | 1.5355457 |
| 5 | 25 | 17 | 0.7580083 |
| 9 | 31 | 163 | 0.5255817 |
| 8 | 8 | 4 | 0.3745522 |
| 10 | 8 | 7 | 0.2070589 |
| 14 | 20 | 27 | 0.1648747 |
| 7 | 184 | 646 | 0.1580656 |
| 4 | 23 | 34 | 0.1273880 |
| 11 | 48 | 165 | 0.1228103 |
| 12 | 84 | 143 | 0.0928364 |
| 6 | 176 | 548 | 0.0770069 |
| 2 | 184 | 349 | 0.0450533 |
| 13 | 56 | 158 | 0.0284724 |
| 1 | 191 | 428 | 0.0023346 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.6802628 | 0.7187125 |
Removed cell cycle genes from HVGs
Cells’s assignment
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 15.6% (184) | 12.6% (350) |
| 2 | 1.9% (23) | 1.2% (34) |
| 3 | 10.2% (120) | 2.4% (68) |
| 4 | 15.9% (188) | 11.3% (314) |
| 5 | 2.1% (25) | 0.6% (17) |
| 6 | 13.5% (160) | 22.8% (634) |
| 7 | 0.7% (8) | 0.1% (4) |
| 8 | 16.7% (197) | 20.2% (561) |
| 9 | 4.1% (48) | 5.9% (165) |
| 10 | 2.5% (29) | 5.3% (147) |
| 11 | 7.5% (88) | 5.1% (141) |
| 12 | 2.4% (28) | 5.5% (152) |
| 13 | 4.7% (55) | 5.7% (158) |
| 14 | 0.7% (8) | 0.3% (7) |
| 15 | 1.7% (20) | 1.0% (27) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 3 | 120 | 68 | 1.7172827 |
| 5 | 25 | 17 | 0.7580083 |
| 12 | 28 | 152 | 0.5563309 |
| 10 | 29 | 147 | 0.4693369 |
| 7 | 8 | 4 | 0.3745522 |
| 6 | 160 | 634 | 0.2669172 |
| 14 | 8 | 7 | 0.2070589 |
| 15 | 20 | 27 | 0.1648747 |
| 11 | 88 | 141 | 0.1311486 |
| 2 | 23 | 34 | 0.1273880 |
| 9 | 48 | 165 | 0.1228103 |
| 4 | 188 | 314 | 0.1135258 |
| 1 | 184 | 350 | 0.0438700 |
| 8 | 197 | 561 | 0.0358759 |
| 13 | 55 | 158 | 0.0343491 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.6730963 | 0.6794929 |
Linear regression
(regressBatches())
With all HVGs
Cells’s assignment
WARNING: The following clusters have zero number of assigned cells in some samples: 2, 6, 9, 10, 15, 16, 17, 21, 23, 24, 25
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 0.1% (1) | 17.8% (494) |
| 2 | 17.4% (205) | 0.0% (0) |
| 3 | 3.9% (46) | 6.4% (178) |
| 4 | 0.3% (4) | 2.1% (57) |
| 5 | 2.3% (27) | 1.1% (31) |
| 6 | 4.5% (53) | 0.0% (0) |
| 7 | 0.4% (5) | 21.2% (588) |
| 8 | 0.2% (2) | 21.6% (600) |
| 9 | 17.2% (203) | 0.0% (0) |
| 10 | 8.0% (95) | 0.0% (0) |
| 11 | 2.5% (29) | 0.1% (2) |
| 12 | 0.7% (8) | 0.3% (7) |
| 13 | 0.4% (5) | 12.5% (348) |
| 14 | 0.2% (2) | 5.6% (157) |
| 15 | 2.5% (30) | 0.0% (0) |
| 16 | 6.4% (75) | 0.0% (0) |
| 17 | 2.2% (26) | 0.0% (0) |
| 18 | 0.7% (8) | 0.1% (4) |
| 19 | 0.3% (3) | 4.5% (125) |
| 20 | 0.3% (3) | 0.3% (8) |
| 21 | 12.9% (152) | 0.0% (0) |
| 22 | 0.2% (2) | 0.9% (26) |
| 23 | 4.3% (51) | 0.0% (0) |
| 24 | 0.0% (0) | 4.6% (128) |
| 25 | 10.7% (126) | 0.0% (0) |
| 26 | 1.7% (20) | 0.9% (26) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 2 | 205 | 0 | 13.2325327 |
| 9 | 203 | 0 | 13.1619306 |
| 8 | 2 | 600 | 12.6706320 |
| 1 | 1 | 494 | 12.2719403 |
| 21 | 152 | 0 | 11.1723650 |
| 7 | 5 | 588 | 10.3256045 |
| 25 | 126 | 0 | 9.9795955 |
| 10 | 95 | 0 | 8.3283815 |
| 13 | 5 | 348 | 7.2604982 |
| 16 | 75 | 0 | 7.0792700 |
| 24 | 0 | 128 | 5.5748370 |
| 6 | 53 | 0 | 5.4626418 |
| 23 | 51 | 0 | 5.2993299 |
| 14 | 2 | 157 | 5.0879976 |
| 19 | 3 | 125 | 3.7005721 |
| 15 | 30 | 0 | 3.3594157 |
| 17 | 26 | 0 | 2.9329660 |
| 11 | 29 | 2 | 2.7829067 |
| 4 | 4 | 57 | 1.2785441 |
| 22 | 2 | 26 | 0.5995513 |
| 18 | 8 | 4 | 0.3745522 |
| 5 | 27 | 31 | 0.3076562 |
| 3 | 46 | 178 | 0.2120390 |
| 12 | 8 | 7 | 0.2070589 |
| 26 | 20 | 26 | 0.1859469 |
| 20 | 3 | 8 | 0.0019810 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.682578 | 0.7571707 |
Removed cell cycle genes from HVGs
Cells’s assignment
WARNING: The following clusters have zero number of assigned cells in some samples: 4, 5, 11, 12, 17, 20, 22, 24
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 0.7% (8) | 23.2% (645) |
| 2 | 0.4% (5) | 18.0% (499) |
| 3 | 4.8% (57) | 6.2% (172) |
| 4 | 18.7% (221) | 0.0% (0) |
| 5 | 9.2% (109) | 0.0% (0) |
| 6 | 0.3% (4) | 2.1% (57) |
| 7 | 2.9% (34) | 0.1% (2) |
| 8 | 4.5% (53) | 0.0% (1) |
| 9 | 0.2% (2) | 19.9% (553) |
| 10 | 0.7% (8) | 0.3% (7) |
| 11 | 15.1% (178) | 0.0% (0) |
| 12 | 14.6% (172) | 0.0% (0) |
| 13 | 0.4% (5) | 12.5% (346) |
| 14 | 0.2% (2) | 5.6% (156) |
| 15 | 1.9% (23) | 1.0% (27) |
| 16 | 0.7% (8) | 0.1% (4) |
| 17 | 2.2% (26) | 0.0% (0) |
| 18 | 0.1% (1) | 4.6% (129) |
| 19 | 0.2% (2) | 0.9% (26) |
| 20 | 4.3% (51) | 0.0% (0) |
| 21 | 10.7% (126) | 0.1% (2) |
| 22 | 0.0% (0) | 4.6% (127) |
| 23 | 1.7% (20) | 0.9% (26) |
| 24 | 5.6% (66) | 0.0% (0) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 4 | 221 | 0 | 13.7806576 |
| 11 | 178 | 0 | 12.2356531 |
| 9 | 2 | 553 | 12.0989086 |
| 12 | 172 | 0 | 12.0001506 |
| 1 | 8 | 645 | 9.3569132 |
| 2 | 5 | 499 | 9.3034438 |
| 21 | 126 | 2 | 9.1394383 |
| 5 | 109 | 0 | 9.1105150 |
| 13 | 5 | 346 | 7.2300754 |
| 24 | 66 | 0 | 6.4540334 |
| 22 | 0 | 127 | 5.5408527 |
| 20 | 51 | 0 | 5.2993299 |
| 8 | 53 | 1 | 5.1393728 |
| 14 | 2 | 156 | 5.0610466 |
| 18 | 1 | 129 | 4.8847532 |
| 7 | 34 | 2 | 3.2564159 |
| 17 | 26 | 0 | 2.9329660 |
| 6 | 4 | 57 | 1.2785441 |
| 19 | 2 | 26 | 0.5995513 |
| 16 | 8 | 4 | 0.3745522 |
| 15 | 23 | 27 | 0.2678992 |
| 10 | 8 | 7 | 0.2070589 |
| 23 | 20 | 26 | 0.1859469 |
| 3 | 57 | 172 | 0.0539253 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.8054033 | 0.732235 |
Rescaling (rescaleBatches())
With all HVGs
Cells’s assignment
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 10.4% (123) | 6.9% (192) |
| 2 | 0.7% (8) | 17.9% (497) |
| 3 | 1.9% (22) | 1.9% (53) |
| 4 | 27.3% (323) | 0.1% (2) |
| 5 | 4.7% (56) | 5.6% (156) |
| 6 | 4.5% (53) | 21.9% (608) |
| 7 | 15.5% (183) | 12.5% (346) |
| 8 | 0.7% (8) | 24.6% (685) |
| 9 | 2.2% (26) | 1.2% (33) |
| 10 | 15.7% (185) | 0.2% (5) |
| 11 | 2.6% (31) | 5.6% (155) |
| 12 | 1.7% (20) | 0.9% (26) |
| 13 | 1.4% (17) | 0.5% (15) |
| 14 | 10.7% (126) | 0.2% (6) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 4 | 323 | 2 | 15.6417269 |
| 10 | 185 | 5 | 10.4018585 |
| 8 | 8 | 685 | 9.7282735 |
| 2 | 8 | 497 | 7.8369010 |
| 14 | 126 | 6 | 7.8174078 |
| 6 | 53 | 608 | 2.3425682 |
| 11 | 31 | 155 | 0.4594092 |
| 13 | 17 | 15 | 0.4015521 |
| 9 | 26 | 33 | 0.2293598 |
| 12 | 20 | 26 | 0.1859469 |
| 1 | 123 | 192 | 0.1560397 |
| 7 | 183 | 346 | 0.0463707 |
| 5 | 56 | 156 | 0.0245889 |
| 3 | 22 | 53 | 0.0003581 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.6740225 | 0.7658615 |
Removed cell cycle genes from HVGs
Cells’s assignment
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 12.4% (146) | 15.7% (436) |
| 2 | 2.4% (28) | 2.2% (60) |
| 3 | 27.6% (326) | 0.1% (3) |
| 4 | 0.3% (4) | 17.6% (489) |
| 5 | 3.0% (35) | 37.9% (1052) |
| 6 | 4.7% (56) | 5.6% (156) |
| 7 | 15.5% (183) | 12.5% (346) |
| 8 | 2.3% (27) | 1.2% (33) |
| 9 | 2.6% (31) | 5.6% (155) |
| 10 | 1.7% (20) | 0.9% (26) |
| 11 | 1.4% (17) | 0.5% (15) |
| 12 | 15.0% (177) | 0.0% (1) |
| 13 | 11.1% (131) | 0.3% (7) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 3 | 326 | 3 | 15.2295849 |
| 12 | 177 | 1 | 11.7113049 |
| 4 | 4 | 489 | 9.7820859 |
| 13 | 131 | 7 | 7.7518446 |
| 5 | 35 | 1052 | 6.0106521 |
| 9 | 31 | 155 | 0.4594092 |
| 11 | 17 | 15 | 0.4015521 |
| 8 | 27 | 33 | 0.2604478 |
| 10 | 20 | 26 | 0.1859469 |
| 1 | 146 | 436 | 0.0550276 |
| 7 | 183 | 346 | 0.0463707 |
| 6 | 56 | 156 | 0.0245889 |
| 2 | 28 | 60 | 0.0060915 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.6792661 | 0.6569117 |
Uncorrected (multiBatchNorm())
With all HVGs
Cells’s assignment
WARNING: The following clusters have zero number of assigned cells in some samples: 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 0.0% (0) | 17.8% (496) |
| 2 | 18.5% (218) | 0.0% (0) |
| 3 | 10.4% (123) | 0.0% (0) |
| 4 | 0.0% (0) | 11.7% (325) |
| 5 | 0.0% (0) | 25.6% (711) |
| 6 | 15.4% (182) | 0.0% (0) |
| 7 | 17.3% (204) | 0.0% (0) |
| 8 | 0.0% (0) | 18.1% (502) |
| 9 | 3.0% (36) | 0.0% (0) |
| 10 | 0.1% (1) | 1.8% (51) |
| 11 | 4.4% (52) | 0.0% (0) |
| 12 | 3.7% (44) | 0.0% (0) |
| 13 | 0.0% (0) | 12.5% (346) |
| 14 | 0.0% (0) | 5.8% (160) |
| 15 | 6.7% (79) | 0.0% (0) |
| 16 | 1.6% (19) | 0.0% (0) |
| 17 | 0.0% (0) | 1.1% (31) |
| 18 | 13.4% (158) | 0.0% (0) |
| 19 | 1.7% (20) | 0.0% (0) |
| 20 | 2.4% (28) | 0.0% (0) |
| 21 | 0.0% (0) | 4.7% (131) |
| 22 | 1.4% (16) | 0.0% (0) |
| 23 | 0.1% (1) | 0.9% (26) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 5 | 0 | 711 | 16.1932513 |
| 2 | 218 | 0 | 13.6800620 |
| 8 | 0 | 502 | 13.5217296 |
| 1 | 0 | 496 | 13.4341285 |
| 7 | 204 | 0 | 13.1972922 |
| 6 | 182 | 0 | 12.3896491 |
| 18 | 158 | 0 | 11.4280451 |
| 13 | 0 | 346 | 10.9558864 |
| 4 | 0 | 325 | 10.5537889 |
| 3 | 123 | 0 | 9.8319303 |
| 15 | 79 | 0 | 7.3434691 |
| 14 | 0 | 160 | 6.5965950 |
| 21 | 0 | 131 | 5.6759811 |
| 11 | 52 | 0 | 5.3813805 |
| 12 | 44 | 0 | 4.7014018 |
| 9 | 36 | 0 | 3.9611720 |
| 20 | 28 | 0 | 3.1488586 |
| 19 | 20 | 0 | 2.2515270 |
| 16 | 19 | 0 | 2.1328930 |
| 10 | 1 | 51 | 1.9771562 |
| 22 | 16 | 0 | 1.7684687 |
| 17 | 0 | 31 | 1.4145495 |
| 23 | 1 | 26 | 0.8302394 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.8069368 | 0.7607062 |
Removed cell cycle genes from HVGs
Cells’s assignment
WARNING: The following clusters have zero number of assigned cells in some samples: 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22
| cluster | pbmc1k | pbmc3k |
|---|---|---|
| 1 | 0.0% (0) | 17.8% (495) |
| 2 | 18.5% (218) | 0.0% (0) |
| 3 | 0.0% (0) | 11.1% (309) |
| 4 | 0.0% (0) | 24.6% (684) |
| 5 | 15.4% (182) | 0.0% (0) |
| 6 | 17.2% (203) | 0.0% (0) |
| 7 | 0.0% (0) | 19.6% (545) |
| 8 | 9.7% (115) | 0.0% (0) |
| 9 | 0.1% (1) | 1.9% (52) |
| 10 | 3.0% (35) | 0.0% (0) |
| 11 | 4.3% (51) | 0.0% (0) |
| 12 | 4.4% (52) | 0.0% (0) |
| 13 | 2.5% (30) | 0.0% (0) |
| 14 | 0.0% (0) | 12.5% (346) |
| 15 | 1.6% (19) | 0.0% (0) |
| 16 | 0.0% (0) | 5.8% (160) |
| 17 | 6.6% (78) | 0.0% (0) |
| 18 | 13.5% (159) | 0.0% (0) |
| 19 | 0.0% (0) | 1.1% (31) |
| 20 | 1.8% (21) | 0.0% (0) |
| 21 | 0.0% (0) | 4.7% (131) |
| 22 | 1.4% (16) | 0.0% (0) |
| 23 | 0.1% (1) | 0.9% (26) |
| Total | 100.0% (1181) | 100.0% (2779) |
Cells’s assignment (plots)
Variance in cluster assignment
The variation in the log-abundances to rank the clusters with the greatest variability in their proportional abundances across batches. We can then focus on batch-specific clusters that may be indicative of incomplete batch correction. Obviously, though, this diagnostic is subject to interpretation as the same outcome can be caused by batch-specific populations; some prior knowledge about the biological context is necessary to distinguish between these two possibilities.
| cluster | pbmc1k | pbmc3k | var |
|---|---|---|---|
| 4 | 0 | 684 | 15.8831655 |
| 7 | 0 | 545 | 14.1288766 |
| 2 | 218 | 0 | 13.6800620 |
| 1 | 0 | 495 | 13.4194561 |
| 6 | 203 | 0 | 13.1619306 |
| 5 | 182 | 0 | 12.3896491 |
| 18 | 159 | 0 | 11.4700212 |
| 14 | 0 | 346 | 10.9558864 |
| 3 | 0 | 309 | 10.2357979 |
| 8 | 115 | 0 | 9.4265038 |
| 17 | 78 | 0 | 7.2781558 |
| 16 | 0 | 160 | 6.5965950 |
| 21 | 0 | 131 | 5.6759811 |
| 12 | 52 | 0 | 5.3813805 |
| 11 | 51 | 0 | 5.2993299 |
| 10 | 35 | 0 | 3.8638409 |
| 13 | 30 | 0 | 3.3594157 |
| 20 | 21 | 0 | 2.3687080 |
| 15 | 19 | 0 | 2.1328930 |
| 9 | 1 | 52 | 2.0211752 |
| 22 | 16 | 0 | 1.7684687 |
| 19 | 0 | 31 | 1.4145495 |
| 23 | 1 | 26 | 0.8302394 |
Rand indices
Rand index is used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
| pbmc1k | pbmc3k |
|---|---|
| 0.8030236 | 0.754878 |
Integration Rand indices
Rand indices are used to evaluate biological heterogeneity preservation by summarizing the agreement between clusterings. This provides a simple metric that we can use to assess the preservation of variation by different correction methods. Larger Rand indices (i.e., closer to 1) are more desirable, though this must be balanced against the ability of each method to actually remove the batch effect.
Show used functions ▾
Show input parameters
Main config
## $PROJECT_NAME
## [1] "Integration of 1k and 3k PBMC"
##
## $PROJECT_DESCRIPTION
## [1] "1000 and 3000 peripheral blood mononuclear cells by 10x Genomics"
##
## $INSTITUTE
## [1] "Example institute"
##
## $LABORATORY
## [1] "Example laboratory"
##
## $PEOPLE
## [1] "Example person 1, Example person 2"
##
## $ORGANISM
## [1] "human"
##
## $ANNOTATION_LIST
## $ANNOTATION_LIST$human
## [1] "org.Hs.eg.db"
##
## $ANNOTATION_LIST$mouse
## [1] "org.Mm.eg.db"
##
##
## $ENSEMBL_SPECIES
## [1] "Homo_sapiens"
##
## $CSS_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/Rmd/common/stylesheet.css"
##
## $BASE_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/output/integration"
##
## $ANNOTATION_DB_FILE
## [1] "/usr/local/lib/R/site-library/org.Hs.eg.db/extdata/org.Hs.eg.sqlite"
##
## $ANNOTATION_PKG
## [1] "org.Hs.eg.db"
##
## attr(,"class")
## [1] "scdrake_list" "list"Integration config
## $INTEGRATION_SOURCES
## $pbmc1k
## $pbmc1k$path
## [1] "../pbmc1k/.drake"
##
## $pbmc1k$path_type
## [1] "drake_cache"
##
## $pbmc1k$description
## [1] "10x Genomics PBMC 1k dataset"
##
## $pbmc1k$hvg_rm_cc_genes
## [1] TRUE
##
## $pbmc1k$hvg_cc_genes_var_expl_threshold
## [1] 5
##
## $pbmc1k$name
## [1] "pbmc1k"
##
##
## $pbmc3k
## $pbmc3k$path
## [1] "../pbmc3k/sce_final_norm_clustering.Rds"
##
## $pbmc3k$path_type
## [1] "sce"
##
## $pbmc3k$description
## [1] "10x Genomics PBMC 3k dataset"
##
## $pbmc3k$hvg_rm_cc_genes
## [1] FALSE
##
## $pbmc3k$hvg_cc_genes_var_expl_threshold
## NULL
##
## $pbmc3k$name
## [1] "pbmc3k"
##
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $HVG_COMBINATION_INT
## [1] "hvg_metric"
##
## $HVG_SELECTION_INT
## [1] "top"
##
## $HVG_SELECTION_VALUE_INT
## [1] 3000
##
## $INTEGRATION_METHODS
## $uncorrected
## $uncorrected$pca_selection_method
## [1] "forced"
##
## $uncorrected$pca_forced_pcs
## [1] 15
##
## $uncorrected$tsne_perp
## [1] 20
##
## $uncorrected$tsne_max_iter
## [1] 1000
##
## $uncorrected$name
## [1] "uncorrected"
##
##
## $rescaling
## $rescaling$pca_selection_method
## [1] "forced"
##
## $rescaling$pca_forced_pcs
## [1] 15
##
## $rescaling$tsne_perp
## [1] 20
##
## $rescaling$tsne_max_iter
## [1] 1000
##
## $rescaling$integration_params
## $rescaling$integration_params$log.base
## [1] 2
##
## $rescaling$integration_params$pseudo.count
## [1] 1
##
##
## $rescaling$name
## [1] "rescaling"
##
##
## $regression
## $regression$pca_selection_method
## [1] "corrected"
##
## $regression$pca_forced_pcs
## [1] 15
##
## $regression$tsne_perp
## [1] 20
##
## $regression$tsne_max_iter
## [1] 1000
##
## $regression$integration_params
## $regression$integration_params$d
## [1] 50
##
##
## $regression$name
## [1] "regression"
##
##
## $mnn
## $mnn$pca_selection_method
## [1] "corrected"
##
## $mnn$pca_forced_pcs
## [1] 15
##
## $mnn$tsne_perp
## [1] 20
##
## $mnn$tsne_max_iter
## [1] 1000
##
## $mnn$integration_params
## $mnn$integration_params$k
## [1] 20
##
## $mnn$integration_params$prop.k
## NULL
##
## $mnn$integration_params$cos.norm
## [1] TRUE
##
## $mnn$integration_params$ndist
## [1] 3
##
## $mnn$integration_params$d
## [1] 50
##
## $mnn$integration_params$merge.order
## NULL
##
## $mnn$integration_params$auto.merge
## [1] TRUE
##
##
## $mnn$name
## [1] "mnn"
##
##
## $harmony
## $harmony$pca_selection_method
## NULL
##
## $harmony$pca_forced_pcs
## NULL
##
## $harmony$tsne_perp
## [1] 20
##
## $harmony$tsne_max_iter
## [1] 1000
##
## $harmony$integration_params
## $harmony$integration_params$dims.use
## [1] 50
##
## $harmony$integration_params$theta
## NULL
##
## $harmony$integration_params$lambda
## NULL
##
## $harmony$integration_params$sigma
## [1] 0.1
##
## $harmony$integration_params$nclust
## NULL
##
## $harmony$integration_params$tau
## [1] 0
##
## $harmony$integration_params$block.size
## [1] 0.05
##
## $harmony$integration_params$max.iter.harmony
## [1] 10
##
## $harmony$integration_params$max.iter.cluster
## [1] 20
##
## $harmony$integration_params$epsilon.cluster
## [1] 1e-05
##
## $harmony$integration_params$epsilon.harmony
## [1] 1e-04
##
##
## $harmony$name
## [1] "harmony"
##
##
## attr(,"class")
## [1] "scdrake_list" "list"
##
## $INTEGRATION_SNN_K
## [1] 10
##
## $INTEGRATION_SNN_TYPE
## [1] "rank"
##
## $INTEGRATION_SNN_CLUSTERING_METHOD
## [1] "walktrap"
##
## $SELECTED_MARKERS_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/selected_markers.csv"
##
## $INTEGRATION_REPORT_RMD_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/Rmd/integration/01_integration.Rmd"
##
## $INTEGRATION_BASE_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/output/integration/01_integration"
##
## $INTEGRATION_SELECTED_MARKERS_OUT_DIR
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/output/integration/01_integration/selected_markers"
##
## $INTEGRATION_REPORT_HTML_FILE
## [1] "/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/output/integration/01_integration/01_integration.html"
##
## $INTEGRATION_KNITR_MESSAGE
## [1] FALSE
##
## $INTEGRATION_KNITR_WARNING
## [1] FALSE
##
## $INTEGRATION_KNITR_ECHO
## [1] FALSE
##
## attr(,"class")
## [1] "scdrake_list" "list"Show runtime info
drake cache directory
/home/rstudio/shared/scdrake_run_tests_20231202_01-1.5.1-bioc3.15-docker/pipeline_outputs/example_data/integration/.drake
Traceback and warnings
## No traceback availableBioconductor version
3.15
External libs
## zlib
## "1.2.11"
## bzlib
## "1.0.8, 13-Jul-2019"
## xz
## "5.2.4"
## PCRE
## "10.34 2019-11-21"
## ICU
## "66.1"
## TRE
## "TRE 0.8.0 R_fixes (BSD)"
## iconv
## "glibc 2.31"
## readline
## "8.0"
## BLAS
## "/usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3"Session info (pretty)
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.2.1 (2022-06-23)
## os Ubuntu 20.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language en
## collate C
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2023-12-02
## pandoc 2.18 @ /usr/local/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date (UTC) lib source
## abind 1.4-5 2016-07-21 [1] RSPM (R 4.2.0)
## AnnotationDbi * 1.58.0 2022-04-26 [1] Bioconductor
## AnnotationFilter * 1.20.0 2022-04-26 [1] Bioconductor
## AnnotationHub 3.4.0 2022-04-26 [1] Bioconductor
## argparser 0.7.1 2021-03-08 [1] RSPM (R 4.2.0)
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.2.0)
## backports 1.4.1 2021-12-13 [1] RSPM (R 4.2.0)
## base64url 1.4 2018-05-14 [1] RSPM (R 4.2.0)
## batchelor 1.12.3 2022-06-23 [1] Bioconductor
## beachmat 2.12.0 2022-04-26 [1] Bioconductor
## beeswarm 0.4.0 2021-06-01 [1] RSPM (R 4.2.0)
## Biobase * 2.56.0 2022-04-26 [1] Bioconductor
## BiocFileCache 2.4.0 2022-04-26 [1] Bioconductor
## BiocGenerics * 0.42.0 2022-04-26 [1] Bioconductor
## BiocIO 1.6.0 2022-04-26 [1] Bioconductor
## BiocManager 1.30.19 2022-10-25 [1] RSPM (R 4.2.0)
## BiocNeighbors 1.14.0 2022-04-26 [1] Bioconductor
## BiocParallel 1.30.4 2022-10-11 [1] Bioconductor
## BiocSingular 1.12.0 2022-04-26 [1] Bioconductor
## BiocVersion 3.15.2 2022-03-29 [1] Bioconductor
## biomaRt 2.52.0 2022-04-26 [1] Bioconductor
## Biostrings 2.64.1 2022-08-18 [1] Bioconductor
## bit 4.0.5 2022-11-15 [1] RSPM (R 4.2.0)
## bit64 4.0.5 2020-08-30 [1] CRAN (R 4.2.0)
## bitops 1.0-7 2021-04-24 [1] CRAN (R 4.2.0)
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## callr 3.7.3 2022-11-02 [1] RSPM (R 4.2.0)
## celldex * 1.6.0 2022-04-28 [1] Bioconductor
## class 7.3-20 2022-01-16 [2] CRAN (R 4.2.1)
## cli * 3.4.1 2022-09-23 [1] RSPM (R 4.2.0)
## cluster 2.1.3 2022-03-28 [2] CRAN (R 4.2.1)
## clustermq 0.8.8 2019-06-05 [1] RSPM (R 4.2.1)
## clustree 0.5.0 2022-06-25 [1] RSPM (R 4.2.0)
## codetools 0.2-18 2020-11-04 [2] CRAN (R 4.2.1)
## colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.2.0)
## conflicted * 1.1.0 2021-11-26 [1] RSPM (R 4.2.0)
## cowplot 1.1.1 2020-12-30 [1] RSPM (R 4.2.0)
## crayon 1.5.2 2022-09-29 [1] RSPM (R 4.2.0)
## curl 4.3.3 2022-10-06 [1] RSPM (R 4.2.0)
## data.table 1.14.6 2022-11-16 [1] RSPM (R 4.2.0)
## DBI 1.1.3 2022-06-18 [1] RSPM (R 4.2.0)
## dbplyr 2.2.1 2022-06-27 [1] RSPM (R 4.2.0)
## DelayedArray * 0.22.0 2022-04-26 [1] Bioconductor
## DelayedMatrixStats 1.18.2 2022-10-13 [1] Bioconductor
## deldir 1.0-6 2021-10-23 [1] RSPM (R 4.2.0)
## DEoptimR 1.0-11 2022-04-03 [1] CRAN (R 4.2.0)
## desc 1.4.2 2022-09-08 [1] RSPM (R 4.2.0)
## devtools 2.4.4 2022-07-20 [1] RSPM (R 4.2.0)
## digest 0.6.30 2022-10-18 [1] RSPM (R 4.2.0)
## downlit 0.4.2 2022-07-05 [1] RSPM (R 4.2.0)
## dplyr 1.0.10 2022-09-01 [1] RSPM (R 4.2.0)
## dqrng 0.3.0 2021-05-01 [1] RSPM (R 4.2.0)
## drake * 7.13.4 2022-08-19 [1] RSPM (R 4.2.0)
## DropletUtils 1.16.0 2022-04-26 [1] Bioconductor
## DT 0.26 2022-10-19 [1] RSPM (R 4.2.0)
## e1071 1.7-12 2022-10-24 [1] RSPM (R 4.2.0)
## edgeR 3.38.4 2022-08-07 [1] Bioconductor
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0)
## ensembldb * 2.20.2 2022-06-16 [1] Bioconductor
## evaluate 0.18 2022-11-07 [1] RSPM (R 4.2.0)
## ExperimentHub 2.4.0 2022-04-26 [1] Bioconductor
## fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0)
## farver 2.1.1 2022-07-06 [1] RSPM (R 4.2.0)
## fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0)
## filelock 1.0.2 2018-10-05 [1] CRAN (R 4.2.0)
## fitdistrplus 1.1-8 2022-03-10 [1] RSPM (R 4.2.0)
## fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0)
## future 1.29.0 2022-11-06 [1] RSPM (R 4.2.0)
## future.apply 1.10.0 2022-11-05 [1] RSPM (R 4.2.0)
## generics 0.1.3 2022-07-05 [1] RSPM (R 4.2.0)
## GenomeInfoDb * 1.32.4 2022-09-06 [1] Bioconductor
## GenomeInfoDbData 1.2.8 2022-05-02 [1] Bioconductor
## GenomicAlignments 1.32.1 2022-07-24 [1] Bioconductor
## GenomicFeatures * 1.48.4 2022-09-20 [1] Bioconductor
## GenomicRanges * 1.48.0 2022-04-26 [1] Bioconductor
## ggbeeswarm 0.6.0 2017-08-07 [1] RSPM (R 4.2.0)
## ggforce 0.4.1 2022-10-04 [1] RSPM (R 4.2.0)
## ggplot2 3.4.0 2022-11-04 [1] RSPM (R 4.2.0)
## ggplotify 0.1.0 2021-09-02 [1] RSPM (R 4.2.0)
## ggraph 2.1.0 2022-10-09 [1] RSPM (R 4.2.0)
## ggrepel 0.9.2 2022-11-06 [1] RSPM (R 4.2.0)
## ggridges 0.5.4 2022-09-26 [1] RSPM (R 4.2.0)
## glmGamPoi 1.8.0 2022-04-26 [1] Bioconductor
## globals 0.16.2 2022-11-21 [1] RSPM (R 4.2.0)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0)
## goftest 1.2-3 2021-10-07 [1] RSPM (R 4.2.0)
## graphlayouts 0.8.4 2022-11-24 [1] RSPM (R 4.2.0)
## gridExtra 2.3 2017-09-09 [1] CRAN (R 4.2.0)
## gridGraphics 0.5-1 2020-12-13 [1] RSPM (R 4.2.0)
## gtable 0.3.1 2022-09-01 [1] RSPM (R 4.2.0)
## harmony 0.1.1 2022-11-14 [1] RSPM (R 4.2.0)
## HDF5Array * 1.24.2 2022-08-02 [1] Bioconductor
## here * 1.0.1 2020-12-13 [1] RSPM (R 4.2.0)
## highr 0.9 2021-04-16 [1] CRAN (R 4.2.0)
## hms 1.1.2 2022-08-19 [1] RSPM (R 4.2.0)
## htmltools 0.5.3 2022-07-18 [1] RSPM (R 4.2.0)
## htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.2.0)
## httpuv 1.6.6 2022-09-08 [1] RSPM (R 4.2.0)
## httr 1.4.4 2022-08-17 [1] RSPM (R 4.2.0)
## ica 1.0-3 2022-07-08 [1] RSPM (R 4.2.0)
## igraph 1.3.5 2022-09-22 [1] RSPM (R 4.2.0)
## interactiveDisplayBase 1.34.0 2022-04-26 [1] Bioconductor
## IRanges * 2.30.1 2022-08-18 [1] Bioconductor
## irlba 2.3.5.1 2022-10-03 [1] RSPM (R 4.2.0)
## janitor 2.1.0 2021-01-05 [1] RSPM (R 4.2.0)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.2.0)
## jsonlite 1.8.4 2022-12-06 [1] RSPM (R 4.2.0)
## kableExtra 1.3.4 2021-02-20 [1] RSPM (R 4.2.0)
## KEGGREST 1.36.3 2022-07-12 [1] Bioconductor
## KernSmooth 2.23-20 2021-05-03 [2] CRAN (R 4.2.1)
## knitr 1.41 2022-11-18 [1] RSPM (R 4.2.0)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.2.0)
## later 1.3.0 2021-08-18 [1] CRAN (R 4.2.0)
## lattice 0.20-45 2021-09-22 [2] CRAN (R 4.2.1)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.2.0)
## leiden 0.4.3 2022-09-10 [1] RSPM (R 4.2.0)
## lifecycle 1.0.3 2022-10-07 [1] RSPM (R 4.2.0)
## limma 3.52.4 2022-09-27 [1] Bioconductor
## listenv 0.8.0 2019-12-05 [1] RSPM (R 4.2.0)
## littler 0.3.17 2023-05-26 [1] Github (eddelbuettel/littler@31aa160)
## lmtest 0.9-40 2022-03-21 [1] RSPM (R 4.2.0)
## locfit 1.5-9.6 2022-07-11 [1] RSPM (R 4.2.0)
## lubridate 1.9.0 2022-11-06 [1] RSPM (R 4.2.0)
## magrittr * 2.0.3 2022-03-30 [1] CRAN (R 4.2.0)
## MASS 7.3-58.1 2022-08-03 [2] RSPM (R 4.2.0)
## Matrix * 1.5-3 2022-11-11 [1] RSPM (R 4.2.0)
## MatrixGenerics * 1.8.1 2022-06-26 [1] Bioconductor
## matrixStats * 0.63.0 2022-11-18 [1] RSPM (R 4.2.0)
## memoise 2.0.1 2021-11-26 [1] CRAN (R 4.2.0)
## metapod 1.4.0 2022-04-26 [1] Bioconductor
## mime 0.12 2021-09-28 [1] CRAN (R 4.2.0)
## miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.2.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0)
## mvtnorm 1.1-3 2021-10-08 [1] CRAN (R 4.2.0)
## nlme 3.1-158 2022-06-15 [2] RSPM (R 4.2.0)
## org.Hs.eg.db 3.15.0 2022-04-11 [1] Bioconductor
## parallelly 1.32.1 2022-07-21 [1] RSPM (R 4.2.0)
## patchwork 1.1.2 2022-08-19 [1] RSPM (R 4.2.0)
## pbapply 1.6-0 2022-11-16 [1] RSPM (R 4.2.0)
## pcaPP 2.0-3 2022-10-24 [1] RSPM (R 4.2.0)
## PCAtools 2.8.0 2022-04-26 [1] Bioconductor
## pheatmap 1.0.12 2019-01-04 [1] RSPM (R 4.2.0)
## pillar 1.8.1 2022-08-19 [1] RSPM (R 4.2.0)
## pkgbuild 1.3.1 2021-12-20 [1] RSPM (R 4.2.0)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0)
## pkgload 1.3.2 2022-11-16 [1] RSPM (R 4.2.0)
## plotly 4.10.1 2022-11-07 [1] RSPM (R 4.2.0)
## plyr 1.8.8 2022-11-11 [1] RSPM (R 4.2.0)
## png 0.1-8 2022-11-29 [1] RSPM (R 4.2.0)
## polyclip 1.10-4 2022-10-20 [1] RSPM (R 4.2.0)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0)
## processx 3.8.0 2022-10-26 [1] RSPM (R 4.2.0)
## profvis 0.3.7 2020-11-02 [1] RSPM (R 4.2.0)
## progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0)
## progressr 0.11.0 2022-09-02 [1] RSPM (R 4.2.0)
## promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.2.0)
## ProtGenerics 1.28.0 2022-04-26 [1] Bioconductor
## proxy 0.4-27 2022-06-09 [1] RSPM (R 4.2.0)
## ps 1.7.2 2022-10-26 [1] RSPM (R 4.2.0)
## purrr 0.3.5 2022-10-06 [1] RSPM (R 4.2.0)
## qs 0.25.4 2022-08-09 [1] RSPM (R 4.2.0)
## R.methodsS3 1.8.2 2022-06-13 [1] RSPM (R 4.2.0)
## R.oo 1.25.0 2022-06-12 [1] RSPM (R 4.2.0)
## R.utils 2.12.2 2022-11-11 [1] RSPM (R 4.2.0)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0)
## RANN 2.6.1 2019-01-08 [1] RSPM (R 4.2.0)
## RApiSerialize 0.1.2 2022-08-25 [1] RSPM (R 4.2.0)
## rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.2.0)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.2.0)
## Rcpp 1.0.9 2022-07-08 [1] RSPM (R 4.2.0)
## RcppAnnoy 0.0.20 2022-10-27 [1] RSPM (R 4.2.0)
## RcppParallel 5.1.5 2022-01-05 [1] RSPM (R 4.2.0)
## RCurl 1.98-1.9 2022-10-03 [1] RSPM (R 4.2.0)
## readr 2.1.3 2022-10-01 [1] RSPM (R 4.2.0)
## remotes 2.4.2 2021-11-30 [1] RSPM (R 4.2.0)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.2.0)
## ResidualMatrix 1.6.1 2022-08-16 [1] Bioconductor
## restfulr 0.0.15 2022-06-16 [1] RSPM (R 4.2.0)
## reticulate 1.26 2022-08-31 [1] RSPM (R 4.2.0)
## rhdf5 * 2.40.0 2022-04-26 [1] Bioconductor
## rhdf5filters 1.8.0 2022-04-26 [1] Bioconductor
## Rhdf5lib 1.18.2 2022-05-15 [1] Bioconductor
## RhpcBLASctl 0.21-247.1 2021-11-05 [1] RSPM (R 4.2.0)
## rjson 0.2.21 2022-01-09 [1] CRAN (R 4.2.0)
## rlang * 1.0.6 2022-09-24 [1] RSPM (R 4.2.0)
## rmarkdown 2.18 2022-11-09 [1] RSPM (R 4.2.0)
## robustbase 0.95-0 2022-04-02 [1] CRAN (R 4.2.0)
## ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.2.0)
## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0)
## rrcov 1.7-2 2022-10-24 [1] RSPM (R 4.2.0)
## Rsamtools 2.12.0 2022-04-26 [1] Bioconductor
## RSQLite 2.2.19 2022-11-24 [1] RSPM (R 4.2.0)
## rstudioapi 0.14 2022-08-22 [1] RSPM (R 4.2.0)
## rsvd 1.0.5 2021-04-16 [1] RSPM (R 4.2.0)
## rtracklayer 1.56.1 2022-06-23 [1] Bioconductor
## Rtsne 0.16 2022-04-17 [1] RSPM (R 4.2.0)
## rvest 1.0.3 2022-08-19 [1] RSPM (R 4.2.0)
## rzmq 0.9.8 2021-05-04 [1] RSPM (R 4.2.0)
## S4Vectors * 0.34.0 2022-04-26 [1] Bioconductor
## sass 0.4.4 2022-11-24 [1] RSPM (R 4.2.0)
## SC3 1.15.1 2023-05-26 [1] Github (gorgitko/SC3@58d73fb)
## ScaledMatrix 1.4.1 2022-09-11 [1] Bioconductor
## scales 1.2.1 2022-08-20 [1] RSPM (R 4.2.0)
## scater 1.24.0 2022-04-26 [1] Bioconductor
## scattermore 0.8 2022-02-14 [1] RSPM (R 4.2.0)
## scDblFinder 1.10.0 2022-04-26 [1] Bioconductor
## VP scdrake * 1.5.1 2023-06-15 [?] Bioconductor (on disk 1.5.0)
## scran 1.24.1 2022-09-11 [1] Bioconductor
## sctransform 0.3.5 2022-09-21 [1] RSPM (R 4.2.0)
## scuttle 1.6.3 2022-08-23 [1] Bioconductor
## sessioninfo 1.2.2 2021-12-06 [1] RSPM (R 4.2.0)
## Seurat 4.3.0 2022-11-18 [1] RSPM (R 4.2.0)
## SeuratObject 4.1.3 2022-11-07 [1] RSPM (R 4.2.0)
## shiny 1.7.3 2022-10-25 [1] RSPM (R 4.2.0)
## SingleCellExperiment 1.18.1 2022-10-02 [1] Bioconductor
## SingleR 1.10.0 2022-04-26 [1] Bioconductor
## snakecase 0.11.0 2019-05-25 [1] RSPM (R 4.2.0)
## sp 1.5-1 2022-11-07 [1] RSPM (R 4.2.0)
## sparseMatrixStats 1.8.0 2022-04-26 [1] Bioconductor
## spatstat.data 3.0-0 2022-10-21 [1] RSPM (R 4.2.0)
## spatstat.explore 3.0-5 2022-11-10 [1] RSPM (R 4.2.0)
## spatstat.geom 3.0-3 2022-10-25 [1] RSPM (R 4.2.0)
## spatstat.random 3.0-1 2022-11-03 [1] RSPM (R 4.2.0)
## spatstat.sparse 3.0-0 2022-10-21 [1] RSPM (R 4.2.0)
## spatstat.utils 3.0-1 2022-10-19 [1] RSPM (R 4.2.0)
## statmod 1.4.37 2022-08-12 [1] RSPM (R 4.2.0)
## storr 1.2.5 2020-12-01 [1] RSPM (R 4.2.0)
## stringfish 0.15.7 2022-04-13 [1] RSPM (R 4.2.0)
## stringi 1.7.8 2022-07-11 [1] RSPM (R 4.2.0)
## stringr 1.5.0 2022-12-02 [1] RSPM (R 4.2.0)
## SummarizedExperiment * 1.26.1 2022-04-29 [1] Bioconductor
## survival 3.3-1 2022-03-03 [2] CRAN (R 4.2.1)
## svglite 2.1.0 2022-02-03 [1] RSPM (R 4.2.0)
## systemfonts 1.0.4 2022-02-11 [1] RSPM (R 4.2.0)
## tensor 1.5 2012-05-05 [1] RSPM (R 4.2.0)
## testthat * 3.1.5 2022-10-08 [1] RSPM (R 4.2.0)
## tibble 3.1.8 2022-07-22 [1] RSPM (R 4.2.0)
## tidygraph 1.2.3 2023-02-01 [1] RSPM (R 4.2.0)
## tidyr 1.2.1 2022-09-08 [1] RSPM (R 4.2.0)
## tidyselect 1.1.2 2022-02-21 [1] RSPM (R 4.2.0)
## timechange 0.1.1 2022-11-04 [1] RSPM (R 4.2.0)
## tweenr 2.0.2 2022-09-06 [1] RSPM (R 4.2.0)
## txtq 0.2.4 2021-03-27 [1] RSPM (R 4.2.0)
## tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0)
## urlchecker 1.0.1 2021-11-30 [1] RSPM (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] RSPM (R 4.2.0)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
## uwot 0.1.14 2022-08-22 [1] RSPM (R 4.2.0)
## vctrs 0.5.1 2022-11-16 [1] RSPM (R 4.2.0)
## vipor 0.4.5 2017-03-22 [1] RSPM (R 4.2.0)
## viridis 0.6.2 2021-10-13 [1] CRAN (R 4.2.0)
## viridisLite 0.4.1 2022-08-22 [1] RSPM (R 4.2.0)
## webshot 0.5.4 2022-09-26 [1] RSPM (R 4.2.0)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
## WriteXLS 6.4.0 2022-02-24 [1] RSPM (R 4.2.0)
## xfun 0.35 2022-11-16 [1] RSPM (R 4.2.0)
## xgboost 1.6.0.1 2022-04-16 [1] RSPM (R 4.2.0)
## XML 3.99-0.13 2022-12-04 [1] RSPM (R 4.2.0)
## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
## XVector 0.36.0 2022-04-26 [1] Bioconductor
## yaml 2.3.6 2022-10-18 [1] RSPM (R 4.2.0)
## yulab.utils 0.0.5 2022-06-30 [1] RSPM (R 4.2.0)
## zlibbioc 1.42.0 2022-04-26 [1] Bioconductor
## zoo 1.8-11 2022-09-17 [1] RSPM (R 4.2.0)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library
##
## V ── Loaded and on-disk version mismatch.
## P ── Loaded and on-disk path mismatch.
##
## ──────────────────────────────────────────────────────────────────────────────Session info (base)
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics stats4 utils methods base
##
## other attached packages:
## [1] celldex_1.6.0 SummarizedExperiment_1.26.1
## [3] ensembldb_2.20.2 AnnotationFilter_1.20.0
## [5] GenomicFeatures_1.48.4 GenomicRanges_1.48.0
## [7] GenomeInfoDb_1.32.4 HDF5Array_1.24.2
## [9] rhdf5_2.40.0 DelayedArray_0.22.0
## [11] MatrixGenerics_1.8.1 matrixStats_0.63.0
## [13] Matrix_1.5-3 drake_7.13.4
## [15] AnnotationDbi_1.58.0 IRanges_2.30.1
## [17] S4Vectors_0.34.0 Biobase_2.56.0
## [19] BiocGenerics_0.42.0 scdrake_1.5.1
## [21] testthat_3.1.5 magrittr_2.0.3
## [23] here_1.0.1 cli_3.4.1
## [25] rlang_1.0.6 conflicted_1.1.0
##
## loaded via a namespace (and not attached):
## [1] rsvd_1.0.5 ica_1.0-3
## [3] svglite_2.1.0 class_7.3-20
## [5] ps_1.7.2 Rsamtools_2.12.0
## [7] lmtest_0.9-40 rprojroot_2.0.3
## [9] crayon_1.5.2 MASS_7.3-58.1
## [11] rhdf5filters_1.8.0 nlme_3.1-158
## [13] WriteXLS_6.4.0 backports_1.4.1
## [15] XVector_0.36.0 ROCR_1.0-11
## [17] irlba_2.3.5.1 callr_3.7.3
## [19] limma_3.52.4 scater_1.24.0
## [21] filelock_1.0.2 stringfish_0.15.7
## [23] xgboost_1.6.0.1 qs_0.25.4
## [25] BiocParallel_1.30.4 rjson_0.2.21
## [27] bit64_4.0.5 glue_1.6.2
## [29] harmony_0.1.1 scDblFinder_1.10.0
## [31] pheatmap_1.0.12 sctransform_0.3.5
## [33] parallel_4.2.1 processx_3.8.0
## [35] vipor_0.4.5 spatstat.sparse_3.0-0
## [37] base64url_1.4 spatstat.geom_3.0-3
## [39] tidyselect_1.1.2 usethis_2.1.6
## [41] argparser_0.7.1 SeuratObject_4.1.3
## [43] fitdistrplus_1.1-8 XML_3.99-0.13
## [45] tidyr_1.2.1 zoo_1.8-11
## [47] GenomicAlignments_1.32.1 xtable_1.8-4
## [49] evaluate_0.18 ggplot2_3.4.0
## [51] scuttle_1.6.3 zlibbioc_1.42.0
## [53] rstudioapi_0.14 miniUI_0.1.1.1
## [55] sp_1.5-1 bslib_0.4.1
## [57] shiny_1.7.3 BiocSingular_1.12.0
## [59] xfun_0.35 pkgbuild_1.3.1
## [61] cluster_2.1.3 tidygraph_1.2.3
## [63] KEGGREST_1.36.3 clustermq_0.8.8
## [65] tibble_3.1.8 interactiveDisplayBase_1.34.0
## [67] ggrepel_0.9.2 listenv_0.8.0
## [69] Biostrings_2.64.1 png_0.1-8
## [71] future_1.29.0 withr_2.5.0
## [73] bitops_1.0-7 ggforce_0.4.1
## [75] plyr_1.8.8 pcaPP_2.0-3
## [77] e1071_1.7-12 dqrng_0.3.0
## [79] RcppParallel_5.1.5 pillar_1.8.1
## [81] cachem_1.0.6 fs_1.5.2
## [83] DelayedMatrixStats_1.18.2 vctrs_0.5.1
## [85] ellipsis_0.3.2 generics_0.1.3
## [87] RApiSerialize_0.1.2 devtools_2.4.4
## [89] tools_4.2.1 beeswarm_0.4.0
## [91] munsell_0.5.0 tweenr_2.0.2
## [93] proxy_0.4-27 fastmap_1.1.0
## [95] compiler_4.2.1 pkgload_1.3.2
## [97] abind_1.4-5 httpuv_1.6.6
## [99] rtracklayer_1.56.1 ExperimentHub_2.4.0
## [101] sessioninfo_1.2.2 plotly_4.10.1
## [103] GenomeInfoDbData_1.2.8 gridExtra_2.3
## [105] edgeR_3.38.4 lattice_0.20-45
## [107] deldir_1.0-6 utf8_1.2.2
## [109] later_1.3.0 dplyr_1.0.10
## [111] BiocFileCache_2.4.0 jsonlite_1.8.4
## [113] storr_1.2.5 scales_1.2.1
## [115] datasets_4.2.1 ScaledMatrix_1.4.1
## [117] pbapply_1.6-0 sparseMatrixStats_1.8.0
## [119] lazyeval_0.2.2 promises_1.2.0.1
## [121] R.utils_2.12.2 goftest_1.2-3
## [123] spatstat.utils_3.0-1 reticulate_1.26
## [125] rmarkdown_2.18 cowplot_1.1.1
## [127] statmod_1.4.37 webshot_0.5.4
## [129] Rtsne_0.16 glmGamPoi_1.8.0
## [131] uwot_0.1.14 igraph_1.3.5
## [133] survival_3.3-1 ResidualMatrix_1.6.1
## [135] yaml_2.3.6 systemfonts_1.0.4
## [137] htmltools_0.5.3 memoise_2.0.1
## [139] profvis_0.3.7 BiocIO_1.6.0
## [141] Seurat_4.3.0 locfit_1.5-9.6
## [143] graphlayouts_0.8.4 PCAtools_2.8.0
## [145] viridisLite_0.4.1 digest_0.6.30
## [147] rrcov_1.7-2 assertthat_0.2.1
## [149] RhpcBLASctl_0.21-247.1 mime_0.12
## [151] rappdirs_0.3.3 SingleR_1.10.0
## [153] RSQLite_2.2.19 yulab.utils_0.0.5
## [155] future.apply_1.10.0 remotes_2.4.2
## [157] data.table_1.14.6 urlchecker_1.0.1
## [159] blob_1.2.3 R.oo_1.25.0
## [161] labeling_0.4.2 splines_4.2.1
## [163] Rhdf5lib_1.18.2 AnnotationHub_3.4.0
## [165] ProtGenerics_1.28.0 RCurl_1.98-1.9
## [167] hms_1.1.2 colorspace_2.0-3
## [169] DropletUtils_1.16.0 BiocManager_1.30.19
## [171] ggbeeswarm_0.6.0 littler_0.3.17
## [173] sass_0.4.4 Rcpp_1.0.9
## [175] RANN_2.6.1 mvtnorm_1.1-3
## [177] txtq_0.2.4 fansi_1.0.3
## [179] tzdb_0.3.0 brio_1.1.3
## [181] parallelly_1.32.1 R6_2.5.1
## [183] grid_4.2.1 ggridges_0.5.4
## [185] lifecycle_1.0.3 bluster_1.6.0
## [187] curl_4.3.3 jquerylib_0.1.4
## [189] leiden_0.4.3 snakecase_0.11.0
## [191] robustbase_0.95-0 desc_1.4.2
## [193] RcppAnnoy_0.0.20 org.Hs.eg.db_3.15.0
## [195] RColorBrewer_1.1-3 spatstat.explore_3.0-5
## [197] stringr_1.5.0 htmlwidgets_1.5.4
## [199] beachmat_2.12.0 polyclip_1.10-4
## [201] biomaRt_2.52.0 purrr_0.3.5
## [203] timechange_0.1.1 gridGraphics_0.5-1
## [205] rvest_1.0.3 globals_0.16.2
## [207] spatstat.random_3.0-1 patchwork_1.1.2
## [209] progressr_0.11.0 batchelor_1.12.3
## [211] codetools_0.2-18 grDevices_4.2.1
## [213] lubridate_1.9.0 metapod_1.4.0
## [215] prettyunits_1.1.1 SingleCellExperiment_1.18.1
## [217] dbplyr_2.2.1 R.methodsS3_1.8.2
## [219] gtable_0.3.1 DBI_1.1.3
## [221] highr_0.9 tensor_1.5
## [223] httr_1.4.4 KernSmooth_2.23-20
## [225] stringi_1.7.8 progress_1.2.2
## [227] reshape2_1.4.4 farver_2.1.1
## [229] viridis_0.6.2 DT_0.26
## [231] xml2_1.3.3 BiocNeighbors_1.14.0
## [233] kableExtra_1.3.4 restfulr_0.0.15
## [235] readr_2.1.3 ggplotify_0.1.0
## [237] scattermore_0.8 BiocVersion_3.15.2
## [239] scran_1.24.1 DEoptimR_1.0-11
## [241] bit_4.0.5 clustree_0.5.0
## [243] spatstat.data_3.0-0 ggraph_2.1.0
## [245] janitor_2.1.0 pkgconfig_2.0.3
## [247] rzmq_0.9.8 knitr_1.41
## [249] downlit_0.4.2 SC3_1.15.1Page generated on 2023-12-02 19:11:24